Is anaphoric reference cooperative?

Two experiments investigated whether the choice of anaphoric expression is affected by the presence of an addressee. Following a context sentence and visual scene, participants described a target scene that required anaphoric reference. They described the scene either to an addressee (Experiment 1) or without an addressee (Experiment 2). When an addressee was present in the task, participants used more pronouns and fewer repeated noun phrases when the referent was the grammatical subject in the context sentence than when it was the grammatical object and they used more pronouns when there was no competitor than when there was. They used fewer pronouns and more repeated noun phrases when a visual competitor was present in the scene than when there was no visual competitor. In the absence of an addressee, linguistic context effects were the same as those when an addressee was present, but the visual effect of the competitor disappeared. We conclude that visual salience effects are due to adjustments that speakers make when they produce reference for an addressee, whereas linguistic salience effects appear whether or not speakers have addressees. </jats:p

Production of referring Eexpressions (PRE-CogSci) 2009 : bridging the gap between computational and empirical approaches to reference

How do speakers refer to entities? This question has been addressed by both psycholinguists and computational linguists. A referring expression is typically defined as one which is produced in order to identify an object or set of objects for a listener or reader, in a relevant domain of discourse. In spite of several decades of research on the topic, our understanding of it is still incomplete, in part due to a lack of communication between psycholinguists and computational linguists, a remarkable state of affairs given the substantial overlap in the topics that these practitioners have investigated. Among these topics, the following have stood out in recent years: Over- and underspecification: Why and how do speakers overspecify when they produce referring expressions? Under what conditions do they underspecify?peer-reviewe

Reference Production as Search:The Impact of Domain Size on the Production of Distinguishing Descriptions

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic ndings in visual search; however, unlike models of reference production, visual search models also predict that search can become very e cient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression { as re ected by speech onset latencies { is in uenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search.peer-reviewe

The head or the verb:Is the lexical boost restricted to the head verb?

Four structural priming experiments investigated whether the lexical boost is due to the repeated head verb of the primed structure or due to the repetition of any verb, testing structural priming of ditransitive structures (The hotel owner decided to loan the tourist a tent/a tent to the tourist). In Experiments 1–3, we manipulated the repetition of the matrix verb (decided) that is not the syntactic head in the primed structure. The results showed abstract structural priming of the embedded ditransitive structure but the repetition of the matrix verb did not boost the priming. In addition to manipulating the repetition of the matrix verb, we also manipulated the head verb of the primed structure (loan) in Experiment 4. It showed a lexical boost with the repetition of the head verb but no boost with the repetition of the matrix verb. These results are consistent with the residual activation model, which only predicts a boost from the verb that is the head of the primed structure. They do not support models which predict that the repetition of any lexical material in a sentence boosts priming

In Defense of Competition During Syntactic Ambiguity Resolution

In a recent series of publications (Traxler et al. J Mem Lang 39:558–92, 1998; Van Gompel et al. J Mem Lang 52:284–07, 2005; see also Van Gompel et al. (In: Kennedy, et al.(eds) Reading as a perceptual process, Oxford, Elsevier pp 621–48, 2000); Van Gompel et al. J Mem Lang 45:225–58, 2001) eye tracking data are reported showing that globally ambiguous (GA) sentences are read faster than locally ambiguous (LA) counterparts. They argue that these data rule out ‘constraint-based’models where syntactic and conceptual processors operate concurrently and syntactic ambiguity resolution is accomplished by competition. Such models predict the opposite pattern of reading times. However, this argument against competition is valid only in conjunction with two standard assumptions in current constraint-based models of sentence comprehension: (1) that syntactic competitions (e.g., Which is the best attachment site of the incoming constituent?) are pooled together with conceptual competitions (e.g., Which attachment site entails the most plausible meaning?), and (2) that the duration of a competition is a function of the overall (pooled) quality score obtained by each competitor. We argue that it is not necessary to abandon competition as a successful basis for explaining parsing phenomena and that the above-mentioned reading time data can be accounted for by a parallel-interactive model with conceptual and syntactic processors that do not pool their quality scores together. Within the individual linguistic modules, decision-making can very well be competition-based

An investigation into the lexical boost with nonhead nouns

In five structural priming experiments, we investigated lexical boost effects in the production of ditransitive sentences. Although the residual activation model of Pickering and Branigan (1998) suggests that a lexical boost should only occur with the repetition of a syntactic licensing head in ditransitive prepositional object (PO)/double object (DO) structures, Scheepers, Raffray, and Myachykov (2017) recently found that it also occurs with the repetition of nouns that are not syntactic heads. We manipulated the repetition of the subject (Experiments 1–3), and the verb phrase (VP) internal arguments (i.e., either theme or recipient, Experiments 4–5) in PO/DO structures. In Experiment 2, the verb was also repeated between prime and target, while in the other experiments it was not. Three different tasks for eliciting the target were employed: picture description via the oral completion of a sentence fragment (Experiments 1–2, and 4), oral completion of a sentence fragment with no visual context (Experiment 3), and oral production of a sentence from a given array of words and no visual context (Experiment 5). Priming occurred in all experiments and was stronger when the verb was repeated (Experiment 2) than when it was not (Experiment 1). However, none of the experiments showed evidence that priming was stronger when either the subject or one of the VP-internal arguments was repeated. These findings support the view that structural information is associated with syntactic heads (i.e., the verb), but not with nonheads such as the subject noun and the VP-internal arguments (Pickering & Branigan, 1998)

Conceptualization in reference production:Probabilistic modeling and experimental testing

In psycholinguistics, there has been relatively little work investigating conceptualization-how speakers decide which concepts to express. This contrasts with work in natural language generation (NLG), a subfield of artificial intelligence, where much research has explored content determination during the generation of referring expressions. Existing NLG algorithms for conceptualization during reference production do not fully explain previous psycholinguistic results, so we developed new models that we tested in three language production experiments. In our experiments, participants described target objects to another participant. In Experiment 1, either size, color, or both distinguished the target from all distractor objects; in Experiment 2, either color, type, or both color and type distinguished it from all distractors; In Experiment 3, color, size, or the border around the object distinguished the target. We tested how well the different models fit the distribution of description types (e.g., "small candle," "gray candle," "small gray candle") that participants produced. Across these experiments, the probabilistic referential overspecification model (PRO) provided the best fit. In this model, speakers first choose a property that rules out all distractors. If there is more than one such property, then they probabilistically choose one on the basis of a preference for that property. Next, they sometimes add another property, with the probability again determined by its preference and speakers' eagerness to overspecify